It’s a computer system, which can analyze data and take decisions almost like a human being. E.g. when a close friend of yours calls you in your mobile from an unknown number, just by hearing their voice you recognize them. You can recognize your friend because you have heard his/her voice several times earlier, and now your brain can match it. Similarly, when you drive a car you know which route to follow since it would be having relatively lesser traffic jam. You can do this since you have driven through these roads several times earlier and now can take a decision based on your experience.
Artificial Intelligence (AI) is a computer program which would analyze these data very fast and help us to gather insights, which would be beneficial for us. Sometimes, AI can even generate some insights which would not have been possible for a person to come up with.
AI is being used in almost all the fields, ranging from Business to Sports. However, in India, it’s getting deployed maximum to take business decisions, popularly known as Data science (using past data to analyze the future). Data Science is used to predict the future behavior of a customer, future of a particular industry, optimize the price of a product, minimize the cost of logistics, minimize employee attrition etc. It is getting used in almost all the branches of a business to enhance the accuracy of a business decision.
It helps the companies to create marketing strategies accurately, which would enable them to stay one step ahead of their competitors and ultimately increase their profit.
According to Harvard Business Review (October 2012 edition), job of a data scientist is the sexiest job of 21st century. According to the McKinsey Global Institute (In a May 2011 report): “By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions. According to Nasscom and market intelligence firm Blueocean, Analytics market in India is going to be $3.03 Billion by 2018-2019 and expected to be double by 2025.According to the research from Everest Group, India holds 35% to 50% of the global analytics service market.
You have to be careful about the training. Most of the training institutes are having some very fancy website and gorgeous marketing campaign. However, they will be teaching you very basic of the analytics rather than going into the real depth which is actually needed in the industry. Usually they also charge quite a hefty amount ranging from 40k-80k for the modules.
Check if your trainer himself/herself is a working professional in the analytics domain. Don’t try to enroll yourself in all the modules they try to sell you. Usually enroll yourself maximum 1–2 software (SAS or R or Python). First you should be learning the coding part of the software (all types of data handling part). Then you should be learning the machine learning techniques - at least 4–6 of them. A reasonable course fees should be around 30K.
In Analytics Educator, we teach SAS (full coding), R (full coding), 6 Machine Learning Algorithms (both supervised and unsupervised) using both SAS and R with 18 case studies. MS Excel, and SQL queries. We also provide CV building classes and Interview techniques and Mock Interviews conducted by people presently employed in at least CMMI level 5 companies. All our faculties are from Analytics Industries with a minimum of 10 years experience.
For more information you may visit: Data Science Courses @ AE
In a single word, I would say it has a stupendous future. In the coming days, every business decision would be based on data. We are generating huge amounts of data each day, and the companies require people to analyze these data so that they can stay one step ahead of their competitors. You may check out the following video for more clear understanding. Scope of Big Data Analytics
It is important to learn SAS, it is a licensed software (very expensive) but very easy to learn with a robust output. There are lots of accounts which are totally dependent on SAS. HSBC, Genpact, United Health, some of the accounts of TCS, are using SAS. (There are other software like R and Python which are being used), still SAS remains an important software to learn.
When you work in the industries, there is nothing called basics. If you know it you should be knowing it well, like a professional, or else you don’t know it. We couldn’t possibly say in front of a client that something wasn’t covered in the syllabus. So, it’s advisable to learn it full. You should be learning first the data handling part of the SAS, and then machine learning algorithms using SAS.
At Analytics Educator (www.analyticseducator.com) in Kolkata, we teach SAS and Machine Learning for a course fee of ₹ 12000 (all inclusive). You may check out the course content by clicking the following link: Data Science Courses @ AE
Getting a job in Data Science is simple, you need to know the subject well (like a professional). Whenever any recruiter goes for an interview (I also go for recruitment for Data Science) they look for knowledge in the candidate, so that they can handle the clients properly.
The real challenge is most of the training institutes are having some fancy website and gorgeous marketing campaign. However, they will be teaching you very basic of the analytics rather than going into the real depth which is actually needed in the industry. However, you couldn’t possibly say in your interview that something wasn’t covered in the syllabus. Usually they also charge quite a hefty amount ranging from ₹ 40k-80k for the modules.
Check if your trainer himself/herself is a working professional in the analytics domain. Don’t try to enroll yourself in all the modules they try to sell you. Usually enroll yourself maximum 1–2 software (SAS or R or Python). First you should be learning the coding part of the software (all types of data handling part). Then you should be learning the machine learning techniques - at least 4–6 of them. A reasonable course fee should be around ₹ 25K - 30K.
One more caveat is beware of the technologies you are learning. If you are learning something like clickview or sportfire (which adds lower value since they are visualization software) or if you learn MS Access or VBA (which adds almost 0 value since these are old technologies and are hardly used now). SAS, Python and R are presently dominating the market along with machine learning and deep learning.
In Analytics Educator, we teach SAS (full coding), R (full coding), 6 Machine Learning Algorithms (both supervised and unsupervised) using both SAS and R with 18 case studies. MS Excel, and SQL queries. We also provide CV building classes and Interview techniques and Mock Interviews conducted by people presently employed in at least CMMI level 5 companies. All our faculties are from Analytics Industries with a minimum of 10 years experience.
For more information you may visit: Data Science Courses @ AE
You don’t have to be experienced to get a job in data analytics. However, you need to know the subject well (like a professional). Whenever any recruiter goes for an interview (I also go for recruitment for Data Science) they look for knowledge in the candidate, so that they can handle the clients properly. You may learn data science on your own by searching it in Google, or by joining an institute. However you learn it, the recruiters don’t care, as long as you know the subject well.
If you want to join an institute then the real challenge is most of the training institutes are having some fancy website and gorgeous marketing campaign. However, they will be teaching you very basic of the analytics rather than going into the real depth which is actually needed in the industry. However, you couldn’t possibly say in your interview that something wasn’t covered in the syllabus. Usually they also charge quite a hefty amount ranging from 40k-80k for the modules.
Check if your trainer himself/herself is a working professional in the analytics domain. Don’t try to enroll yourself in all the modules they try to sell you. Usually enroll yourself maximum 1–2 software (SAS or R or Python). First you should be learning the coding part of the software (all types of data handling part). Then you should be learning the machine learning techniques - at least 4–6 of them. A reasonable course fee should be around 25K - 30K.
One more caveat is beware of the technologies you are learning. If you are learning something like clickview or sportfire (which adds lower value since they are visualization software) or if you learn MS Access or VBA (which adds almost 0 value since these are old technologies and are hardly used now). SAS, Python and R are presently dominating the market along with machine learning and deep learning.
You may check some of our free videos on data science at the following link: FREE Analytcs Resources @ AE
Experience is a parameter which indicates that the associate is likely to have knowledge, but it’s not an ultimate certificate. That’s why, every associate has to go through an interview process where their knowledge and competencies are being tested.
Usually the companies would like to hire the experienced associates. However, it doesn’t mean that a fresher or someone having experience in another industry (be it insurance or manufacturing or sales etc) can’t get into Data Science. The trick is, even if you are not an experienced person, you should be well versed in Data Science like a pro (should know the software like R/SAS/Python and the machine learning algorithms; deep learning would be an icing on the cake but till now not compulsory). In short you should be knowing everything about some of the algorithms and coding.
Once you become thorough with the subject, then start applying. You should apply for all the jobs getting posted over the websites. Don’t even think about the requirement of the experience, let the recruiter decide on it. Any recruiter will go for inexperienced candidates as well provided they know really well about their subject. Surely you will start getting calls, which you need to convert into a job offer by showcasing your skill sets.
You may also write to us for any other information on Data Science at analyticseducator@gmail.com or visit us www.analyticseducator.com
Show your boss the benefit of data science. Try to do a small PoC with your client’s data, show the analysis to your boss. The analysis might be a simple one - a descriptive analysis, (doesn’t need to be a machine learning or deep learning) and have him showcase the same to the client. Once the client gets interested then, perhaps, they would like you to do more stuff like this. Let your boss handle the billing part, and you concentrate on the technical part.
Always, have your boss in the presentation, and let him take little credit for the work. Most importantly, talk about the future scope - if you get more data then what all the algorithms can be deployed there.
The easiest task which can be deployed to showcase your skills would be - Robotic Automation using R & Descriptive Analytics on client’s data.
For more on robotic automation you may visit the Facebook page - Analytics Educator - Automation using R.
Every business/corporate house wants to analyze the data of their sales volume, customers, suppliers, competitors etc. to gain meaningful insight that would enable them to stay one step ahead of their competitors. In other words, every business house wants to gain inside information about their customers so that they can minimize their cost and maximize their profit. That’s the ultimate motive of any kind of business. Big Data Analytics or Business Intelligence is the tool that digs into the data and extracts these meaningful insights/information.
You have to be careful about the training. Most of the training institutes have some very fancy websites and gorgeous marketing campaigns. However, they will be teaching you very basic of the analytics rather than going into the real depth which is actually needed in the industry. Usually they also charge quite a hefty amount ranging from 40k-80k for the modules.
Check if your trainer himself/herself is a working professional in the analytics domain. Don’t try to enroll yourself in all the modules they try to sell you. Usually enroll yourself maximum 1–2 software (SAS or R or Python). First you should be learning the coding part of the software (all types of data handling part). Then you should be learning the machine learning techniques - at least 4–6 of them. A reasonable course fee should be around 30K.
In Analytics Educator, we teach Python (full coding), R (full coding), 6 Machine Learning Algorithms (both supervised and unsupervised) using both Python and R with 18 case studies. MS Excel, and SQL queries. We also provide CV building classes and Interview techniques and Mock Interviews conducted by people presently employed in at least CMMI level 5 companies. All our faculties are from Analytics Industries with a minimum of 10 years’ experience.
For more information you may visit: We have both online and offline course Data Science Courses @ AE
You don’t have to have a certificate to get a job in data analytics. However, you need to know the subject well (like a professional). Whenever any recruiter goes for an interview (I also go for recruitment for Data Science) they look for knowledge in the candidate, so that they can handle the clients properly. You may learn data science on your own by searching it in Google, or by joining an institute. However you learn it, the recruiters don’t care, as long as you know the subject well.
If you want to join an institute then the real challenge is most of the training institutes are having some fancy website and gorgeous marketing campaign. However, they will be teaching you very basic of the analytics rather than going into the real depth which is actually needed in the industry. However, you couldn’t possibly say in your interview that something wasn’t covered in the syllabus. Usually they also charge quite a hefty amount ranging from 40k-80k for the modules.
Check if your trainer himself/herself is a working professional in the analytics domain. Don’t try to enroll yourself in all the modules they try to sell you. Usually enroll yourself maximum 1–2 software (SAS or R or Python). First you should be learning the coding part of the software (all types of data handling part). Then you should be learning the machine learning techniques - at least 4–6 of them. A reasonable course fee should be around 25K - 30K.
One more caveat is beware of the technologies that you are learning. If you are learning something like clickview or sportfire (which adds lower value since they are visualization software) or if you learn MS Access or VBA (which adds almost 0 value since these are old technologies and are hardly used now). SAS, Python and R are presently dominating the market along with machine learning and deep learning.
You may check some of our free videos on data science at the following link FREE Analytcs Resources @ AE
Data science training has become a very lucrative business nowadays. Most of the training institutes have some very fancy websites and gorgeous marketing campaigns. Their course fees will mostly depend on the tenure and the infrastructure. Some of them will call their course as MSc. and charge around 2.5L with duration of 1 year. They will also charge more if they have some trainers from well-known institutes like IITs and increase their course fees.
However, most of the institutes will be teaching you very basic of the analytics rather than going into the real depth which is actually needed in the industry. Usually they also charge quite a hefty amount ranging from 40k-500k for the modules.
What you need to do if you want to join a Data Science training institute:
Check if your trainer himself/herself is a working professional in the analytics domain. Don’t try to enroll yourself in all the modules they try to sell you. Usually enroll yourself maximum 1–2 software (SAS or R or Python; my personal advice is R and Python). First you should be learning the coding part of the software (all types of data handling part). Then you should be learning the machine learning techniques - at least 4–6 of them. A reasonable course fee should be around 30K.
Ideal sample course content would be:
Python (full coding), R (full coding), 6–7 Machine Learning Algorithms (both supervised and unsupervised) using both Python and R with case studies. MS Excel, and SQL queries. CV building classes and Interview techniques and Mock Interviews would be a bonus. It’s better if the faculties are from Analytics Industries with a minimum of 7-10 years experience.
You may also visit our website: Data Science Courses @ AE
In Data science, primarily 3 topics are tested in an interview.
In total, one should be knowing the software, statistics, & machine learning algorithms. In addition, sometimes (40% times) they will also ask puzzles. These are the skill sets one needs to be well versed with in order to crack an interview.
You may also check the following link where we have discussed the same in more details: How to learn Data Science and get a job
Getting a job in Data Science is simple; you need to know the subject well (like a professional). Whenever any recruiter goes for an interview (I also go for recruitment for Data Science) they look for knowledge in the candidate, so that they can handle the clients properly. It doesn’t matter, even if someone is having a gap (unless the HR policy restricts such candidates) or coming from a different background.
The real challenge is most of the training institutes are having some fancy website and gorgeous marketing campaign. However, they will be teaching you very basic of the analytics rather than going into the real depth which is actually needed in the industry. However, you couldn’t possibly say in your interview that something wasn’t covered in the syllabus. Usually they also charge quite a hefty amount ranging from 40k-80k for the modules.
Check if your trainer himself/herself is a working professional in the analytics domain. Don’t try to enroll yourself in all the modules they try to sell you. Usually enroll yourself maximum 1–2 software (SAS or R or Python). First you should be learning the coding part of the software (all types of data handling part). Then you should be learning the machine learning techniques - at least 4–6 of them. A reasonable course fee should be around 25K - 30K.
One more caveat is beware of the technologies that you are learning. If you are learning something like clickview or sportfire (which adds lower value since they are visualization software) or if you learn MS Access or VBA (which adds almost 0 value since these are old technologies and are hardly used now). SAS, Python and R are presently dominating the market along with machine learning and deep learning.
In Data science, primarily 3 topics are tested in an interview.
In total, one should be knowing the software, statistics, & machine learning algorithms. In addition, sometimes (40% times) they will also ask puzzles. These are the skill sets one needs to be well versed with in order to crack an interview.
You may also check the following link where we have discussed the same in more details: How to learn Data Science and get a job
I understand, there is a lot of hype over Python during the recent times. However, Python is a multi-tasking tool which is used for Website building, developing video games, or even data analytics. It’s an excel tool for Data Science, but it’s not the only tool for the same.
There is another software - R programming which is equally capable for deploying machine learning algorithms which is essential for data science and other data handling activities.
There are some advantages and disadvantages for both R and Python. Python is more efficient in handling big data than R, and it is very useful for unstructured data analysis like Image Classification, Speech recognition, natural language processing etc.
However, R is more efficient with robust output while deploying machine learning algorithms. Any sort of statistical analysis can be done in R.
Statistical analysis can also be done in Python, just like R, but there are certain algorithms like Linear Regression, Logistic Regression, etc where the R output is more comprehensive than Python and you have to write less code in R.
There is certainly no compulsion to learn Python to work in data science, but certainly it’s advisable to learn it along with R.
There is a significant difference between Data Science and Python. Let’s understand!
What is Data Science?
Every business/corporate house wants to analyze the data of their sales volume, customers, suppliers, competitors etc. to gain meaningful insight that would enable them to stay one step ahead of their competitors. In other words, every business house wants to gain inside information about their customers so that they can minimize their cost and maximize their profit. That’s the ultimate motive of any kind of business. Big Data Analytics or Business Intelligence is the procedure that digs into the data and extracts these meaningful insights/information.
What is Python?
It is the software which uses the process of Data Science (we call them Machine Learning Algorithms and Deep Learning Algorithms) and helps you to gain insights from the data to make more money.
So you see, Data Science and Python are complementary. You should be learning them both if you want to work as a Data Scientist.
You may also check the following link where we have discussed the same in more details: How to learn Data Science and get a job
You have to be careful about the training. Most of the training institutes have some very fancy websites and gorgeous marketing campaigns. However, they will be teaching you very basic of the analytics rather than going into the real depth which is actually needed in the industry. Usually they also charge quite a hefty amount ranging from 40k-500k for the modules.
Check if your trainer himself/herself is a working professional in the analytics domain. Don’t try to enroll yourself in all the modules they try to sell you. Usually enroll yourself maximum 1–2 software (SAS or R or Python). First you should be learning the coding part of the software (all types of data handling part). Then you should be learning the machine learning techniques - at least 4–6 of them. A reasonable course fee should be around 30K.
In Data science, primarily 3 topics are tested in an interview.
In total, one should be knowing the software, statistics, & machine learning algorithms. In addition, sometimes (40% times) they will also ask puzzles. These are the skill sets one needs to be well versed with in order to crack an interview.
For a model data science course curriculum you may refer to Data Science Courses @ AE
There is a fundamental difference between Analysis and Analytics.
Analysis is the study and extracting insights about the past; what has already happened. Suppose, I have company, and analyzed that the north region has earned the maximum revenue in a particular year, however the east zone has been getting the highest profit margin. All these are analysis, where we are talking about the past, what has already happened.
Analytics is insights about the future. If I suggest a client to price their product at a particular level so that they will have 12% extra sale. If a consultant asks the client to buy 1,000 tons of steel since he has a forecast there would be a spike in the demand in the coming months. In these scenarios, we are talking about what is likely to happen in the future. This is also called predictive analytics and the algorithms through which we predict is called machine learning algorithms.
You may also check the following link where we have discussed the same in more details: How to learn Data Science and get a job
I understand, there is a lot of hype over Python during the recent times. However, Python is a multi-tasking tool which is used for Website building, developing video games, or even data analytics. It’s an excel tool for Data Science, but it’s not the only tool for the same.
If you want to learn Data Analysis then you should be learning all different types of data handling techniques like filter & subset, merge, frequency distribution, pivot table, different functions of Python (numeric, character & date) etc. Presently, you have correctly identified, you are learning some of the codes which you are never going to use in data science.
You should also be learning statistics and the concepts. How a data scientist uses statistics to help a business take a decision. After that you should be learning Machine Learning algorithms through which a business predicts the future to build management strategies.
If you want to know a model course curriculum to learn Data Science with Python then you may visit: Data Science Courses @ AE
In Data science, primarily 3 skill sets are tested in an interview.
In total, one should know the software, statistics, & machine learning algorithms. In addition, sometimes (40% times) they will also ask puzzles. These are the skill sets one needs to be well versed with in order to crack an interview.
You may also check the following link where we have discussed the same in more details: How to learn Data Science and get a job
Each one of these languages are having their own set of advantages and disadvantages. SAS is the easiest to learn, while most expensive. Most of the companies are moving away from SAS. You shouldn't go with it.
R and Python are both open source and FREE. Their coding are pretty similar to each other. You may start any one of them. R is more robust in terms of statistical analysis and comprehensive output for machine learning while Python is better suited for unstructured data. There is a lot of overlap between these 2 software as well for deploying machine learning algorithms. Though you may start with any one of them, I would have marginal preference towards R due to its better capabilities with statistical analysis and user friendly output.
Once you have learnt R, you may consider learning Python though. You may have a look into my website for all the topics (both data handling and machine learning) you should learn in R and Python. Data Science Courses @ AE
An interviewer usually tests 3 aspects – Software coding, Machine Learning Knowledge, & Analytical abilities. Software coding is tested by asking questions from the data handling part which may include inbuilt functions, data wrangling, data visualization, etc. In other words, they might give you a situation and ask you to say the code to get it done. E.g.1 there is a variable with numbers and characters; which code would you use to delete the rows with characters and keep the rows with only numeric values. E.g.2 How do you show the values of a variable which has appeared at least 4 times. E.g.3 How do you clean a variable of phone numbers along with some text and punctuation by mistake. One should be absolutely thorough with the coding part and should be capable of extracting all information from the data by transforming it in every possible way. Please keep checking our blog, where we would be uploading a list of probable interview questions soon.
Proficiency in coding and conceptual clarity both are required to crack an interview. These skills are complementary – if I need to deploy a machine learning algorithm of logistic regression then first I need to clean the data, which would require coding skills. I might have to impute the missing values by predicting them which would require conceptual understanding of how random forest helps in predicting missing values and impute them. (Click here for the tutorial on missing values). One cannot neglect any of these skills in order to get a job in data science.